Reconfigurable Computing Systems Engineering: Virtualization of Computing Architecture

Reconfigurable Computing Systems Engineering: Virtualization of Computing Architecture describes the organization of reconfigurable computing system (RCS) architecture and discusses the pros and cons of different RCS architecture implementations. Providing a solid understanding of RCS technology and where it's most effective, this book:

• Details the architecture organization of RCS platforms for application-specific workloads


• Covers the process of the architectural synthesis of hardware components for system-on-chip (SoC) for the RC


• Explores the virtualization of RCS architecture from the system and on-chip levels


• Presents methodologies for RCS architecture run-time integration according to mode of operation and rapid adaptation to changes of multi-parametric constraints


• Includes illustrative examples, case studies, homework problems, and references to important literature

A solutions manual is available with qualifying course adoption.

Reconfigurable Computing Systems Engineering: Virtualization of Computing Architecture offers a complete road map to the synthesis of RCS architecture, exposing hardware design engineers, system architects, and students specializing in designing FPGA-based embedded systems to novel concepts in RCS architecture organization and virtualization.

Lev Kirischian, Ph.D, P.Eng, Member IEEE, has been affiliated with Ryerson University, Canada for 18 years. His research involves dynamically reconfigurable computing systems, automated architectural synthesis of data-stream processors, and workload-adaptive and self-healing reconfigurable architectures. He participated in the research and development of the first-generation Soviet supercomputers with reconfigurable architectures in the 1980s, FPGA-based segment of COFDM modulation technology for digital audio/video broadcasting systems for satellite and terrestrial networks (used in the SiriusXM satellite radio network), workload adaptive and self-restorable space-borne embedded computer platforms, and 3D-panoramic machine vision systems, among other technologies. In the last decade, he has developed and taught several courses associated with high-performance and reconfigurable computing as well as high-level synthesis of application-specific processors.